K. Frouchni, L. Briand, Y. Labiche, L. Grady, and R. Subramanyan (2011)

Automating Image Segmentation Verification and Validation by Learning Test Oracles

Information and Software Technology (Elsevier) 53(12)

Context: An Image Segmentation Algorithm delineates (an) object(s) of interest in an image. Its output is referred to as a segmentation. Developing these algorithms is a manual, iterative process involving repetitive verification and validation tasks. This process is time-consuming and depends on the availability of experts, who may be a scarce resource (e.g., medical experts). Objective: We see the task of validating and verifying these algorithms as an instance of the oracle problem and provide a framework referred to as Image Segmentation Automated Oracle (ISAO) to help automate this process, saving substantial resources and improving efficiency. Method: ISAO uses machine learning to construct an automated oracle. During the initial learning phase, segmentations from the first few (optimally two) versions of the segmentation algorithm are manually verified by experts. The similarity of successive segmentations of the same images is also measured in various ways. This information is then fed to a machine learning algorithm to construct a classifier that distinguishes between consistent and inconsistent segmentation pairs (as determined by an expert) based on the values of the similarity measures associated with each segmentation pair. Once the accuracy of the classifier is deemed satisfactory to support a consistency determination, the classifier is then used to determine whether the segmentations that are produced by subsequent versions of the algorithm under test, are (in)consistent with already verified segmentations from previous versions. This information is then used to automatically draw conclusions about the correctness of the segmentations. Results: We have successfully applied this approach to 3D segmentations of the cardiac left ventricle obtained from CT scans and have obtained promising results (accuracies of 95%). Conclusion: ISAO has demonstrated the ability to increase the quality and testing efficiency of image segmentation algorithms. The framework also gives informative feedback to the developer as the segmentation algorithm evolves and provides a systematic means of testing different parametric configurations of the algorithm.
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